class_docstr: Recurrent neural network transducer (RNN-T) joint network.
  
  Args:
      input_dim (int): source and target input dimension.
      output_dim (int): output dimension.
      activation (str, optional): activation function to use in the joiner.
          Must be one of ("relu", "tanh"). (Default: "relu")
      biasing (bool): perform biasing
      deepbiasing (bool): perform deep biasing
      attndim (int): dimension of the biasing vector hptr
code: def __init__(
      self,
      input_dim: int,
      output_dim: int,
      activation: str = "relu",
      biasing: bool = False,
      deepbiasing: bool = False,
      attndim: int = 1,
  ) -> None:
      super().__init__()
      self.linear = torch.nn.Linear(input_dim, output_dim, bias=True)
      self.biasing = biasing
      self.deepbiasing = deepbiasing
      if self.biasing and self.deepbiasing:
          self.biasinglinear = torch.nn.Linear(
              attndim, input_dim, bias=True
          )
          self.attndim = attndim
      if activation == "relu":
          self.activation = torch.nn.ReLU()
      elif activation == "tanh":
          self.activation = torch.nn.Tanh()
      else:
          raise ValueError(f"Unsupported activation {activation}")
  
imports: import math
  from typing import Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import Conformer, RNNT
  from torchaudio.models.rnnt import _Joiner, _Predictor, _TimeReduction, _Transcriber
label_desc: Use image node_id 1 to create a new _JoinerBiasing object from inherited base classes: torch.nn with example: obj = _JoinerBiasing(input_dim, output_dim, activation, biasing, deepbiasing, attndim)